🎬 Movie Recommender System

This is a content-based Movie Recommender System built with Python, Streamlit, and machine learning techniques. It recommends movies based on similarity with the one selected by the user.

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🚀 Features

• 🔍 Recommends 5 similar movies based on the selected movie
• 🎞️ Displays movie posters using TMDB API
• ⚙️ Built using cosine similarity on TF-IDF/CountVectorized data
• 📦 Hosted on Streamlit Cloud
• 🧠 Trained on a dataset from Kaggle (processed and saved using .pkl files)

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🛠️ Tech Stack

• Python
• Streamlit
• Scikit-learn
• Pandas, NumPy
• Pickle (for storing preprocessed data)
• Git LFS (for handling large .pkl files)

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🧾 Files in This Repository

File	Description
streamlit_app.py	Main file to run the app
similarity.pkl	Precomputed cosine similarity matrix (via LFS)
movies_dict.pkl	Preprocessed movie dataset dictionary
.gitattributes	Git LFS tracking configuration
requirements.txt	Python dependencies for deployment
README.md	Project description

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🌐 Live Demo

👉 https://movie-recommender-system-mlproject.streamlit.app/

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🔧 How to Run Locally

1. Clone the repository

git clone https://github.com/sonall99/Movie-Recommender-System.git
cd Movie-Recommender-System

2. Install dependencies

pip install -r requirements.txt

3. Run the app

streamlit run streamlit_app.py